Design of Cf/SiCf/Si3N4f multifiber layered composite with enhanced electromagnetic wave absorption properties.
Published In: Journal of the American Ceramic Society, 2025, v. 108, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhu, Henghai; Hu, Yue; Men, Xiaolong; Zhang, Qinzhao; Pang, Liang; Xiao, Peng; Luo, Heng; Zhou, Wei; Li, Yang 3 of 3
Abstract
Traditional fiber‐reinforced composites with single‐layered electrical conductivity (EC) face limitations with respect to electromagnetic wave absorption (EWA) bandwidth and strength. This study introduces a novel Cf/SiCf/Si3N4f layered composite (multifiber layered composite, MFLC), prepared via fiber alignment and curing, which capitalizes on the distinct ECs of carbon fibers (Cf), silicon carbide fibers (SiCf), and silicon nitride fibers (Si3N4f) to address these limitations. The Si3N4f layer enhances the impedance matching, deepening EW penetration and curtailing reflection. The conductive Cf and SiCf layers lead to substantial energy dissipation through conduction loss. Electric field simulations confirmed the regulatory effect of Si3N4f layer on EC, thereby facilitating the optimization of impedance matching. MFLCs achieved a minimum reflection loss of −68.52 dB and an effective absorption bandwidth of 8.23 GHz in the X–Ku band. The optimally matched composites demonstrated exceptional EWA performance, attaining the radar cross section reduction of up to 149.9%. The MFLCs hold significant promise as a novel class of lightweight, highly efficient, and wide‐bandwidth EW absorbers. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of the American Ceramic Society. 2025/04, Vol. 108, Issue 4, p1
- Document Type:Article
- Subject Area:Physics
- Publication Date:2025
- ISSN:0002-7820
- DOI:10.1111/jace.20301
- Accession Number:183980160
- Copyright Statement:Copyright of Journal of the American Ceramic Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.